Mac mini (2023)
Geekbench AI Score
3172
Single Precision Score
4768
Half Precision Score
3949
Quantized Score
Geekbench AI 1.0.0 for macOS AArch64
Result Information
| User | kfendt |
| Upload Date | August 16 2024 03:38 PM |
| Views | 9 |
System Information
| AI Information | |
|---|---|
| Framework | Core ML |
| Backend | CPU |
| Device | Apple M2 Pro |
| System Information | |
|---|---|
| Operating System | macOS 14.6.1 (Build 23G93) |
| Model | Mac mini (2023) |
| Model ID | Mac14,12 |
| Motherboard | Mac14,12 |
| CPU Information | |
|---|---|
| Name | Apple M2 Pro |
| Topology | 1 Processor, 10 Cores |
| Identifier | Apple M2 Pro |
| Base Frequency | 3.48 GHz |
| Cluster 1 | 6 Cores |
| Cluster 2 | 4 Cores |
| Memory Information | |
|---|---|
| Size | 32.00 GB |
AI Performance
| Workload | Accuracy | Score | |
|---|---|---|---|
|
Image Classification (SP)
|
100% |
1479
275.0 IPS |
|
|
Image Classification (HP)
|
100% |
1840
342.1 IPS |
|
|
Image Classification (Q)
|
99% |
1829
341.2 IPS |
|
|
Image Segmentation (SP)
|
100% |
1308
21.2 IPS |
|
|
Image Segmentation (HP)
|
100% |
1296
21.0 IPS |
|
|
Image Segmentation (Q)
|
100% |
1299
21.1 IPS |
|
|
Pose Estimation (SP)
|
100% |
6293
7.34 IPS |
|
|
Pose Estimation (HP)
|
100% |
14956
17.5 IPS |
|
|
Pose Estimation (Q)
|
96% |
12699
14.9 IPS |
|
|
Object Detection (SP)
|
100% |
1600
126.9 IPS |
|
|
Object Detection (HP)
|
97% |
2248
179.0 IPS |
|
|
Object Detection (Q)
|
94% |
1704
135.8 IPS |
|
|
Face Detection (SP)
|
100% |
3029
36.0 IPS |
|
|
Face Detection (HP)
|
100% |
4378
52.0 IPS |
|
|
Face Detection (Q)
|
100% |
2861
34.0 IPS |
|
|
Depth Estimation (SP)
|
100% |
5555
42.8 IPS |
|
|
Depth Estimation (HP)
|
99% |
8640
66.8 IPS |
|
|
Depth Estimation (Q)
|
96% |
4271
33.0 IPS |
|
|
Style Transfer (SP)
|
100% |
17047
21.9 IPS |
|
|
Style Transfer (HP)
|
100% |
26935
34.6 IPS |
|
|
Style Transfer (Q)
|
100% |
26662
34.3 IPS |
|
|
Image Super-Resolution (SP)
|
100% |
3828
141.4 IPS |
|
|
Image Super-Resolution (HP)
|
100% |
4802
177.3 IPS |
|
|
Image Super-Resolution (Q)
|
98% |
3610
133.7 IPS |
|
|
Text Classification (SP)
|
100% |
2274
3.04 KIPS |
|
|
Text Classification (HP)
|
100% |
3356
4.48 KIPS |
|
|
Text Classification (Q)
|
93% |
3354
4.50 KIPS |
|
|
Machine Translation (SP)
|
100% |
2124
36.6 IPS |
|
|
Machine Translation (HP)
|
98% |
4616
79.8 IPS |
|
|
Machine Translation (Q)
|
98% |
4558
78.8 IPS |
